Data Understanding

Before trying to build a classifier we first need to do some exploration on and understanding of the data. In the first place we need to decide which information we are going to use for the classifier. For example the genre for each artist probably will be similar and thus will not be useful data. Furthermore we have two possible sets of features we can use to train the classifier with:

• Track Features, these features are returned by the get_track_audio_features() method of the spotifyr package. This method is also used to get the track features in the get_artist_audio_features() and get_album_audio_features() methods. These features are values that say something about the song in a whole, thus we will get 1 feature value per song.
• Track Analyis, these features are obtained using the get_track_audio_analysis() method fromt the spotifyr package. The analysis features are quite a bit more extensive than the track features, thus will probably contain a lot more information about the song. However this means more data, which will take up more disk space, take longer to obtain from Spotify, make the classifiation training take more time and make the model quite a bit more complex (since we now need to add a time dimension to our model).

Because of the reasons described above I’m first going to focus on creating a model created with the track features. If I fail to create a good model with these features I’m going to take a look at the track analysis features.

The track features include many features including the following numeric features that may be useful:

• Danceability
• Energy
• Key
• Loudness
• Mode
• Speechiness
• Accousticness
• Instrumentalness
• Liveness
• Valence
• Tempo

This is a lot of data in which some features may be very similar for all songs. It is useless to include this data in the trainingsdata for the classifier since it wouldn’t provide good information to distinguish two songs from each other, let alone different artists.

To give a good insight in these features and get a quick overview of which of these may be show some clear differences between the artists, I have combined all songs from all artists into one dataset. I have plotted each of them here (I would have made them interactive however due to limitations of the plotly package they are just images. From these plots you can observe that the Mode features is not useful. Some other features don’t seem to show any clear patterns on their own too. That is why I have plotted each feature relative to the other features. If one of these plots already show clusters we probably only need to use these two features to train a classifier with. However I have put these plots in a separate document because of the amount (121) and because no plot seems to show any clear clusters. So although the scatterplots did seem to show some nice patterns, these patterns seem to be very similar among the artists. This thus means we need to perform Principal Component Analysis. I will elaborate more on this in the Data Preparation section. If the PCA provides us with good clusters we know that we can quite easily build a classifier, however if the PCA doesn’t provide us with any noticable clusters it may be possible that the data still can be clustered, but in higher dimensions. This, however, is quite hard to visualize, thus then I will probably just feed the data to the classifier and hopefully it will be able to draw relations between the features and the artist.

Dimensionality Reduction

Before we can feed the data to the classifier we first need to prepare the data. One part of data preparation is data reduction. This means that we reduce the initial dataset to be only data we are going to use for the classifier. Since we are going to use the track features we can all discard all data other than these features. I combined this data only into a new data frame and saved that as my new corpus. Previously I mentioned removing the Mode feature from our data as well, however since we are now going to perform PCA I will keep this feature for now. Of course we still need to include the Artist in our reduced dataset since we need to use that data as classes for the model.

Principal Component Analysis

As mentioned in the Data Understanding we need to apply Principal Component Analysis on the data since the features on their own or relative to one other feature didn’t show any good clusters. Principal component analysis means that we reduce the data to a new dataset where each column is an information rich column that captures as much possible variation from the initial data. This data may be even better to use than the features on their own since the PCA data will be more dense in information, and will contain only the relevant parts of the features. First of all I made a PCA of the data, I have plotted the first two against each other (see Figure 1).

As you can see, there is still no clear separation between the artists, if any we see that they all seem to be very close in terms of PC1 and PC2. However since the PCA consists, in our case, of 11 principal components, we might not see all differences between the artists. Thus we need a way to see whether all principal components can be used to desinguish the artists.

K-Means Clustering

We can use clustering for this and k-means clustering in particular. This clustering algorithm first assigns each data point to a random cluster. Then it iteratively calculates the centerpoint of each cluster and assigns each data point to the cluster with the nearest centerpoint, this is done using some distance formula that handles multidimensional data (like Euclidean distance, Manhattan distance). To compare the standard features with the principal components I have applied k-means clustering on both the features data and principal components data with 7 clusters (we have 7 artists).

If the data can be effectively clustered we would see 7 separate circles, and in each cirlce there will be only one type of points. However, as you can see in Figure 2 and Figure 3 (can be found interactive in the next to stories) most data points are all in one big cluster and the circles of all clusters all cross each other. We do see that using the Principal Components as data for the clustering algorithm did help to separate one cluster from all others. But if we examine this cluster we see that 6 out of the 7 artists have songs in this cluster.

[1] "Noisecontrollers"   "Headhunterz"        "Brennan Heart"     
[4] "Da Tweekaz"         "Sub Zero Project"   "D-Block & S-te-Fan"

(Upcoming) Subsetting the data for the classifier

Since we are going to train a classifier we also need to seperate the data into two subsets:

• A trainingset, containing about 80% of the data. This data will be used to train the classifier.
• A testset, containing the remaining 20% of the data. This data will be used to test the classifier.

However, since taking only 20% of the data as validation data, this means we get only 140 songs to validate the classifier. In the best scenario this will mean 20 songs per artist, however since we don’t have an equal amount of songs per artist we will most likely not get 20 songs per artist in the validation data.

For this reason I’m going to perform cross validation on the data with 5 batches. This means that I’m going to shuffle the data and then divide the data into 5 parts (thus each part is 20% of the data). After that I will repeatedly take one part as testset and the other parts as trainingset. This way all data will be once testdata and 4 times trainingsdata, resulting in a model that has seen more data and thus is less overfitted on that data, hopefully giving a model that can classify novel songs better.